Clustering of single-cell multi-omics data with a multimodal deep learning method

Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.

Automated summary

Single-cell multimodal sequencing technologies provide an opportunity to analyze different types of data in the same cell simultaneously. However, combining multiple data sources for clustering analysis of single-cell multimodal data remains a challenge. In this study, a novel deep learning method called scMDC is developed, which explicitly models different data sources and learns latent features for clustering analysis. The experimental results show that scMDC outperforms existing methods on single-cell multimodal datasets and has linear scalability for analyzing large datasets.